Chan whye quine

Nevertheless, until now, infinite-width networks have been limited to at most two hidden layers. To address this shortcoming, we study the initialisation requirements of these networks and show that the main challenge for constructing them is defining the appropriate sampling distributions for the weights. Based on these observations, we propose a principled approach to weight initialisation that correctly accounts for the functional nature of the hidden layer activations and facilitates the construction of arbitrarily many infinite-width layers, thus enabling the construction of arbitrarily deep infinite-width networks.

The main idea of our approach is to iteratively reparametrise the hidden-layer activations into appropriately defined reproducing kernel Hilbert spaces and use the canonical way of constructing probability distributions over these spaces for specifying the required weight distributions in a principled way. Furthermore, we examine the practical implications of this construction for standard, finite-width networks.

In particular, we derive a novel weight initialisation scheme for standard, finite-width networks that takes into account the structure of the data and information about the task at hand. We propose a novel variational inference method for deep Gaussian processes GPs , which combines doubly stochastic variational inference with variational Fourier features, an inter-domain approach that replaces inducing points-based inference with a framework that harnesses RKHS Fourier features.

First experiments have shown that inter-domain deep Gaussian processes are able to achieve levels of predictive performance superior to shallow GPs and alternative deep GP models. We construct a Bayesian distribution regression formalism that accounts for this uncertainty, improving the robustness and performance of the model when group sizes vary. We can obtain MAP estimates for some models with backpropagation, while the full propagation of uncertainty requires MCMC-based inference.

We demonstrate our approach on an illustrative toy dataset as well as a challenging age prediction problem. Identifying causal relationships among a set of variables is a fundamental problem in many areas of science.

In this paper, we present a novel general-purpose causal inference method, Kernel Conditional Deviance for Causal Inference KCDC , for inferring causal relationships from observational data. In particular, we propose a novel interpretation of the well-established notion of asymmetry between cause and effect.

Based on this, we derive an asymmetry measure using the framework of representing conditional distributions in reproducing kernel Hilbert spaces thus providing the basis for causal discovery.

We demonstrate the versatility and robustness of our method across several synthetic datasets. Causal discovery methods for geoscientific time series datasets aim at detecting potentially causal statistical associations that cannot be explained by other variables in the dataset.

A large-scale complex system like the Earth presents major challenges for methods such as Granger causality. In particular, its high dimensionality and strong autocorrelations lead to low detection power, distorting biases, and unreliable hypothesis tests.

Here we introduce a reliable method that outperforms current approaches in detection power and overcomes detection biases, making it suitable to detect even weak causal signals in large-scale geoscientific datasets. In experiments, our approach is able to solve learning problems where a single message operator is required for multiple, substantially different data sets logistic regression for a variety of classification problems , where it is essential to accurately assess uncertainty and to efficiently and robustly update the message operator.

We propose a nonparametric two-sample test with cost linear in the number of samples. Our test statistic uses differences in smoothed characteristic functions: these are able to distinguish a larger class of alternatives than the non-smoothed characteristic functions used in previous linear-time tests, while being much faster than the current state-of-the-art tests based on kernels or distances, which are quadratic in the sample size.

This performance advantage is retained even in high dimensions, and in cases where the difference in distributions is not observable in low order statistics. Preprints S. Chau, J. Gonzalez, and D. Severin, D. Lennon, L. Camenzind, F. Vigneau, F.

Fedele, D. Jirovec, A. Ballabio, D. Chrastina, G. Isella, M. Carballido, S. Svab, A. Kuhlmann, F. Braakman, S. Geyer, F. Froning, H. Moon, M. Osborne, D. Sejdinovic, G. Katsaros, D. Briggs, and N. Wild, M. Kanagawa, and D. Zhu, A. Howes, O. Rischard, D. Sejdinovic, and S. Rindt, R. Hu, D.

Steinsaltz, and D. Li, W. Su, and D. Chau, M. Cucuringu, and D. Zhang, S. Filippi, S. Flaxman, and D. Li, A. Perez-Suay, G. Camps-Valls, and D.

Kanagawa, P. Hennig, D. Sejdinovic, and B. Strathmann, D. Sejdinovic, and M. Bradley, S. Kuriwaki, M. Isakov, D. Sejdinovic, X. Meng, and S. Chau, S.

Bouabid, and D. Ton, J. Gonzalez, Y. Teh, and D. Hu, G. Nicholls, and D. Fernandez, A. Gretton, D. Rindt, and D. Nguyen, S. Orbell, D. Lennon, H. Moon, F. Vigneau, L. Camenzind, L. Yu, D. Briggs, M. Sejdinovic, and N. Caterini, R. Cornish, D. Sejdinovic, and A. Li, J. Ton, D. Oglic, and D. Pu, S. Chau, X. Dong, and D. Rindt, D. Sejdinovic, and D. Ton, L. Chan, Y. Sejdinovic, and K. Hu and D. Blair, R. Bassett, L. Bastin, L. Beevers, M. Borrajo, M. Brown, S.

Dance, A. Dionescu, L. Edwards, M. Ferrario, R. Fraser, H. Fraser, S. Gardner, P. Henrys, T. Hey, S. Homann, C. Huijbers, J. Hutchison, P. Jonathan, R. Lamb, S. Laurie, A. Leeson, D. Leslie, M. McMillan, V. Nundloll, O. Oyebamiji, J. Phillipson, V. Pope, R. Prudden, S. Reis, M. Salama, F. Samreen, D. Sejdinovic, W. Simm, R. Street, L. Thornton, R. Towe, J. Hey, M. Vieno, J. Waller, and J. Steinsaltz, A kernel and optimal transport based test of independence between covariates and right-censored lifetimes , International Journal of Biostatistics , Moon, V.

Nguyen, F. Zumbuehl, G. Briggs, D. Ares, Quantum device fine-tuning using unsupervised embedding learning , New Journal of Physics , vol. Andrew D. Moon, D. Lennon, J. Kirkpatrick, N. Yu, F. Vigneau, D. Sejdinovic, E. Laird, and N. Ares, Machine learning enables completely automatic tuning of a quantum device faster than human experts , Nature Communications , vol. A and Sejdinovic, D.

Rudner, D. Sejdinovic, and Y. Runge, P. Nowack, M. Kretschmer, S. Law, P. Zhao, L. Chan, J. Huang, and D. Raj, H. Law, D. Camps-Valls, D. Sejdinovic, J. Runge, and M. Briol, C. Oates, M. Girolami, M. Osborne, and D. Caterini, A. Doucet, and D. Cameron, T. Lucas, S. Flaxman, K.

Battle, and K. Mitrovic, D. Ton, S. Flaxman, D. Sutherland, D. Filippi, A. Gretton, and D. Flaxman, Y. Law, C. Yau, and D. Schuster, H. Strathmann, B. Paige, and D. Vukobratovic, D. Jakovetic, V. Skachek, D. Bajovic, D. Karabulut Kurt, C. Hollanti, and I. Bajovic, and D. Franchi, J. Angulo, and D.

Paige, D. Sejdinovic, and F. Cunningham, and S. Park, W. Jitkrittum, and D. Sejdinovic, S. Livingstone, Z. Szabo, and A. Chwialkowski, A. Ramdas, D. Kurth-Nelson, G. Barnes, D. Sejdinovic, R. Dolan, and P. Jitkrittum, A. Gretton, N. Heess, S. Eslami, B. Lakshminarayanan, D. Sejdinovic, and Z. Chwialkowski, D. Sejdinovic, H. Strathmann, M. Lomeli, C. Andrieu, and A. Johnson, D. Cruise, R. Piechocki, and A. Sejdinovic, A. Gretton, and W. Sejdinovic, B. Sriperumbudur, A. Gretton, and K.

Gretton, B. Sriperumbudur, D. Strathmann, S. Balakrishnan, M. Pontil, and K. Sriperumbudur, and K. Piechocki and D. Muller, D. Sejdinovic, and R. Dai, D. A research-intensive university with an entrepreneurial dimension, NUS is ranked consistently as one of the world's top universities.

We offer the most extensive Review - the best rsort stay we have ever had. Located in Chinatown, the street probably makes reference to the ancestral Information on promotions, airfares, travel tips, online reservation of hotels and travel packages.

Login Register Login with Facebook. Chan An Kiau. About Us How to get. Companies Products. See the RePEc data check for the archive and series. Rakkiyappan High order well-balanced discontinuous Galerkin methods for Euler equations at isentropic equilibrium state under gravitational fields pp.

Allouch , D. Sbibih and M. Tahrichi Semi-implicit second order schemes for numerical solution of level set advection equation on Cartesian grids pp. Hayat, Sadia Asad, M. Mustafa, A. Alsaedi, Applied Mathematics and Computation 12—22'' pp. Segade and J.